Instructions to use cathuriges/tigerlily-r3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cathuriges/tigerlily-r3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="cathuriges/tigerlily-r3")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("cathuriges/tigerlily-r3")
model = AutoModelForImageTextToText.from_pretrained("cathuriges/tigerlily-r3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cathuriges/tigerlily-r3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cathuriges/tigerlily-r3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cathuriges/tigerlily-r3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cathuriges/tigerlily-r3

SGLang

How to use cathuriges/tigerlily-r3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cathuriges/tigerlily-r3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cathuriges/tigerlily-r3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cathuriges/tigerlily-r3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cathuriges/tigerlily-r3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cathuriges/tigerlily-r3 with Docker Model Runner:
```
docker model run hf.co/cathuriges/tigerlily-r3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

tigerlily-r3

This is a merge of pre-trained language models created using mergekit.

Merge Details

Bumped Tiger up a skosh from (unpublished) R2. Mix seems good now. Project continues.

Merge Method

This model was merged using the Task Arithmetic merge method using grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated as a base.

Models Merged

The following models were included in the merge:

tame-tiger
withered-calla

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: tame-tiger
    parameters:
      weight: 0.75
  - model: withered-calla
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated
parameters:
  normalize: true